Semiconductor device with metal gate fill structure

ABSTRACT

A semiconductor process system etches gate metals on semiconductor wafers. The semiconductor process system includes a machine learning based analysis model. The analysis model dynamically selects process conditions for an etching process. The process system then uses the selected process conditions data for the next etching process.

BACKGROUND Technical Field

The present disclosure relates to the field of semiconductorfabrication. The present disclosure relates more particularly to etchingprocesses for semiconductor fabrication.

Description of the Related Art

There has been a continuous demand for increasing computing power inelectronic devices including smart phones, tablets, desktop computers,laptop computers and many other kinds of electronic devices. Integratedcircuits provide the computing power for these electronic devices. Oneway to increase computing power in integrated circuits is to increasethe number of transistors and other integrated circuit features that canbe included for a given area of semiconductor substrate.

To continue decreasing the size of features in integrated circuits,various thin-film deposition techniques, etching techniques, and otherprocessing techniques are implemented. These techniques can form verysmall features. However, these techniques also face serious difficultiesin ensuring that the features are properly formed.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIGS. 1A-1M are cross-sectional views of an integrated circuit atvarious stages of processing, according to one embodiment.

FIG. 2A is an illustration of a semiconductor process system, accordingto one embodiment.

FIG. 2B is a graph illustrating fluid flow during a cycle of an atomiclayer etching process.

FIG. 3A is a block diagram of a control system of a semiconductorprocess system.

FIG. 3B is a block diagram of an analysis model, according to oneembodiment.

FIG. 4 is flow diagram of a process for training an analysis model of acontrol system, according to one embodiment.

FIG. 5 is a flow diagram of a process for performing a thin-filmdeposition process in conjunction with an analysis model, according toone embodiment.

DETAILED DESCRIPTION

In the following description, many thicknesses and materials aredescribed for various layers and structures within an integrated circuitdie. Specific dimensions and materials are given by way of example forvarious embodiments. Those of skill in the art will recognize, in lightof the present disclosure, that other dimensions and materials can beused in many cases without departing from the scope of the presentdisclosure.

The following disclosure provides many different embodiments, orexamples, for implementing different features of the described subjectmatter. Specific examples of components and arrangements are describedbelow to simplify the present description. These are, of course, merelyexamples and are not intended to be limiting. For example, the formationof a first feature over or on a second feature in the description thatfollows may include embodiments in which the first and second featuresare formed in direct contact, and may also include embodiments in whichadditional features may be formed between the first and second features,such that the first and second features may not be in direct contact. Inaddition, the present disclosure may repeat reference numerals and/orletters in the various examples. This repetition is for the purpose ofsimplicity and clarity and does not in itself dictate a relationshipbetween the various embodiments and/or configurations discussed.

Further, spatially relative terms, such as “beneath,” “below,” “lower,”“above,” “upper” and the like, may be used herein for ease ofdescription to describe one element or feature's relationship to anotherelement(s) or feature(s) as illustrated in the figures. The spatiallyrelative terms are intended to encompass different orientations of thedevice in use or operation in addition to the orientation depicted inthe figures. The apparatus may be otherwise oriented (rotated 90 degreesor at other orientations) and the spatially relative descriptors usedherein may likewise be interpreted accordingly.

In the following description, certain specific details are set forth inorder to provide a thorough understanding of various embodiments of thedisclosure. However, one skilled in the art will understand that thedisclosure may be practiced without these specific details. In otherinstances, well-known structures associated with electronic componentsand fabrication techniques have not been described in detail to avoidunnecessarily obscuring the descriptions of the embodiments of thepresent disclosure.

Unless the context requires otherwise, throughout the specification andclaims that follow, the word “comprise” and variations thereof, such as“comprises” and “comprising,” are to be construed in an open, inclusivesense, that is, as “including, but not limited to.”

The use of ordinals such as first, second and third does not necessarilyimply a ranked sense of order, but rather may only distinguish betweenmultiple instances of an act or structure.

Reference throughout this specification to “one embodiment” or “anembodiment” means that a particular feature, structure or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. Thus, the appearances of the phrases “in one embodiment” or“in an embodiment” in various places throughout this specification arenot necessarily all referring to the same embodiment. Furthermore, theparticular features, structures, or characteristics may be combined inany suitable manner in one or more embodiments.

As used in this specification and the appended claims, the singularforms “a,” “an,” and “the” include plural referents unless the contentclearly dictates otherwise. It should also be noted that the term “or”is generally employed in its sense including “and/or” unless the contentclearly dictates otherwise.

Embodiments of the present disclosure provide thin films of reliablethickness and composition. Embodiments of the present disclosure utilizemachine learning techniques to adjust thin-film etching processparameters between etching processes or even during etching processes.Embodiments of the present disclosure utilize machine learningtechniques to train an analysis model to determine process parametersthat should be implemented for a next thin-film etching process or evenfor a next phase of a current thin-film etching process. The result isthat thin-film etching processes produce thin films having remainingthicknesses and compositions that reliably fall within targetspecifications. Integrated circuits that include the thin films will nothave performance problems that can result if the thin films are notproperly formed. Furthermore, batches of semiconductor wafers will haveimproved yields and fewer scrapped wafers.

FIGS. 1A-M are cross-sectional views of an integrated circuit 100 atsuccessive intermediate stages of processing, according to oneembodiment. FIGS. 1A-M illustrated an exemplary process producing anintegrated circuit that includes multiple types of transistors. Eachtype of transistor has a threshold voltage that is different than theother types of transistors. FIGS. 1A-M illustrate how all of these typesof transistors can be formed in a simple and effective process inaccordance with principles of the present disclosure. Other processsteps and combinations of process steps can be utilized withoutdeparting from the scope of the present disclosure. FIGS. 1A-Millustrate a process for forming N-channel transistors of an ultra-lowthreshold voltage type, a low threshold voltage type, and a standardthreshold voltage type. The process for forming P-channel transistors ofthese types is substantially the same as the process for forming theN-channel transistors except that the processes particular to theultra-low threshold voltage transistors and the standard thresholdvoltage transistors are reversed for the P-channel process with respectto the N-channel process. This will be set forth in further detailbelow.

FIG. 1A is a cross-sectional diagram of an integrated circuit 100 at anintermediate stage of processing, according to one embodiment. The viewof FIG. 1A illustrates a transistor 102, a transistor 104, and atransistor 106. Each of the transistors 102, 104, and 106 are formed inthe same integrated circuit 100. The transistors 102, 104, and 106 eachhave different threshold voltages. At this stage of processing shown inFIG. 1A, the transistors 102, 104, and 106 have the same structures.Accordingly, in FIG. 1A, the reference numbers applied to the transistor102 apply to the analogous structures in the transistors 104 and 106.The transistor 102 corresponds to an ultra-low threshold voltagetransistor. The transistor 104 corresponds to a low threshold voltagetransistor. The transistor 106 corresponds to a standard thresholdvoltage transistor. Put another way, the threshold voltage of thetransistor 106 is greater than the threshold voltage of the transistor104. The threshold voltage of the transistor 104 is greater than thethreshold voltage of the transistor 102.

The integrated circuit 100 includes a semiconductor substrate 102. Inone embodiment, the substrate 102 includes a single crystallinesemiconductor layer on at least a surface portion. The substrate 102 maycomprise a single crystalline semiconductor material such as, but notlimited to Si, Ge, Site, GaAs, InSb, GaP, GaSb, InAlAs, InGaAs, GaSbP,GaAsSb and InP. In this embodiment, the substrate 102 is made of Si. Thesubstrate 102 may include in its surface region, one or more bufferlayers (not shown). The buffer layers can serve to gradually change thelattice constant from that of the substrate to that of the source/drainregions. The buffer layers may be formed from epitaxially grown singlecrystalline semiconductor materials such as, but not limited to Si, Ge,GeSn, SiGe, GaAs, InSb, GaP, GaSb, InAlAs, InGaAs, GaSbP, GaAsSb, GaN,GaP, and InP. In a particular embodiment, the substrate 102 comprisessilicon germanium (Site) buffer layers epitaxially grown on the siliconsubstrate 102. The germanium concentration of the Site buffer layers mayincrease from 30 atomic % germanium for the bottom-most buffer layer to70 atomic % germanium for the top-most buffer layer. The substrate 102may include various regions that have been suitably doped withimpurities (e.g., p-type or n-type conductivity). The dopants are, forexample boron (BF₂) for an n-type transistor and phosphorus for a p-typetransistor.

The integrated circuit 100 includes a shallow trench isolation 108. Theshallow trench isolation 108 can be utilized to separate groups oftransistor structures formed in conjunction with the semiconductorsubstrate 110. The shallow trench isolation 108 can include a dielectricmaterial. The dielectric material for the shallow trench isolation 108may include silicon oxide, silicon nitride, silicon oxynitride (SiON),SiOCN, SiCN, fluorine-doped silicate glass (FSG), or a low-K dielectricmaterial, formed by LPCVD (low pressure chemical vapor deposition),plasma-CVD or flowable CVD. Other materials and structures can beutilized for the shallow trench isolation 108 without departing from thescope of the present disclosure.

The integrated circuit 100 includes a plurality of semiconductornanosheets 111 or nanowires. The semiconductor nanosheets 111 are layersof semiconductor material. The semiconductor nanosheets 111 correspondto the channel regions of the transistors 102, 104, and 106. The stackedsemiconductor nanosheets 111 are formed over the substrate 102. Thesemiconductor nanosheets 111 may include one or more layers of Si, Ge,Site, GaAs, InSb, GaP, GaSb, InAlAs, InGaAs, GaSbP, GaAsSb or InP. Inone embodiment, the semiconductor nanosheets 111 are the samesemiconductor material as the substrate 110. Other semiconductormaterials can be utilized for the semiconductor nanosheets 111 withoutdeparting from the scope of the present disclosure.

In FIG. 1A, each transistor 102, 104, and 106 have three semiconductornanosheets 111. However, in practice, each transistor 102, 104, and 106may have many more semiconductor nanosheets 111 than three. For example,each transistor 102 may include between 8 and 20 semiconductornanosheets 111. Other numbers of semiconductor nanosheets 111 can beutilized without departing from the scope of the present disclosure.

The semiconductor nanosheets 111 can have thicknesses between 2 nm and50 nm. In one embodiment, the semiconductor nanosheets 111 havethicknesses between 5 nm and 20 nm. The semiconductor nanosheets 111 canhave other thicknesses without departing from the scope of the presentdisclosure.

The integrated circuit 100 includes date structures 114. The gatestructures 114 are positioned between the semiconductor nanosheets 111.In practice, the gate structures 114 surround the nanosheets 111, exceptwhere the nanosheets 111 meet the source and drain regions 112. At thestage of processing shown in FIG. 1A, each gate structure 114 includes ahigh-K dielectric layer 120, an interfacial dielectric layer 122, afirst gate metal 118, and a void 116. The high-K dielectric layer 120and the interfacial dielectric layer 122 collectively form a gatedielectric of the transistors 102, 104, and 106. The high-K dielectriclayer 120 and the interfacial dielectric layer 122 physically separatethe semiconductor nanosheets 111 from the metal or metals of the gatestructures 114. At the stage of processing shown in FIG. 1A, only thefirst gate metal 118 is present. However, after further stages ofprocessing, other gate metals will be present in the gate structures114. The high-K dielectric layer 120 and the interfacial dielectriclayer 122 isolate these (late metals from the semiconductor nanosheets111 corresponding to the channel regions of the transistors.

The interfacial layer may be used in order to create a good interfacebetween the semiconductor nanosheets 111 and the gate structures 114, aswell as to suppress the mobility degradation of the channel carrier ofthe semiconductor device. The interfacial dielectric layer 122 caninclude a dielectric material such as silicon oxide, silicon nitride, orother suitable dielectric material. The interfacial dielectric layer 122can be formed by a thermal oxidation process, a chemical vapordeposition (CVD) process, or an atomic layer deposition (ALD) process.The interfacial dielectric layer 122 can have a thickness between 0.5 nmand 3 nm. Other materials, deposition processes, and thicknesses can beutilized for the interfacial dielectric layer 122 without departing fromthe scope of the present disclosure.

The high-€K dielectric layer 120 includes one or more layers of adielectric material, such as silicon oxide, silicon nitride, HfO₂,HfSiO, HfSiON, HfTaO, HfTiO, HfZrO, zirconium oxide, aluminum oxide,titanium oxide, hafnium dioxide-alumina (HfO₂—Al₂O₃) alloy, othersuitable high-K dielectric materials, and/or combinations thereof. Thehigh-K dielectric layer 120 may be formed by CVD, ALD, or any suitablemethod. In one embodiment, the high-K dielectric layer 120 is formedusing a highly conformal deposition process such as ALD in order toensure the formation of a gate dielectric layer having a uniformthickness around each semiconductor nanosheet 111. In one embodiment,the thickness of the high-K dielectric is in a range from about 1 nm toabout 6 nm. Other thicknesses, deposition processes, and materials canbe utilized for the high-K dielectric layer 120 without departing fromthe scope of the present disclosure.

In one embodiment, the first date metal 118 includes titanium nitride.The first gate metal 118 can be deposited using physical vapordeposition (PVD), atomic layer deposition, chemical vapor deposition, orother suitable deposition processes. The first gate metal 118 can have athickness between 2 nm and 20 nm. Other materials, deposition processes,and thicknesses can be utilized for the first gate metal 118 withoutdeparting from the scope of the present disclosure.

The gate structures 114 can also include sidewall spacers 124. Thesidewall spacers 124 can include multiple layers of dielectric material.The multiple layers of dielectric material can include silicon nitride,SiON, SiOCN, SiCN, silicon oxide, or other dielectric materials. Otherdielectric materials can be utilized for the sidewall spacers 124without departing from the scope of the present disclosure.

The source and drain regions 112 includes semiconductor material. Thesource and drain regions 112 can be grown epitaxially from thesemiconductor nanosheets 111. The source and drain regions 112 can beepitaxially grown from the semiconductor nanosheets 111 and othersemiconductor layers that previously occupied the locations of the gatestructures 114. The source and drain regions 112 can be doped withN-type dopant species in the case of N-type transistors. The source anddrain regions 112 can be doped with P-type dopant species in the case ofP-type transistors.

The integrated circuit 100 includes an interlayer dielectric layer 128positioned on the source and drain regions 112. The interlayerdielectric layer 128 can include one or more of silicon oxide, siliconnitride, SICOH, SiOC, or an organic polymer. Other types of dielectricmaterials can be utilized for the interlayer dielectric layer 128without departing from the scope of the present disclosure.

The integrated circuit 100 includes trenches 138 formed in theinterlayer dielectric layer 128. The trenches 138 correspond to futuremetal gate region of the transistors 102, 104, and 106. The trench 138is bounded by first sidewall spacers 134. The first sidewall spacers 134are the same material as the sidewall spacers 124 of the gate structures114 and can be formed in the same deposition process. The bottom of thetrench 138 includes the interfacial dielectric layer 122. The portion ofthe interfacial dielectric layer 122 formed on the top surface of theuppermost semiconductor nanosheet 111 at the bottom of the trench 138 isformed in the same process as the interfacial dielectric layer 122 ofthe gate structures 114. The high-K dielectric layer 120 is formed onthe top surface of the interlayer dielectric layer 128, on the sidewallsof the trench 138, and on top of the portion of the interfacialdielectric layer 122 at the bottom of the trench 138. The first gatemetal 118 is formed on the high-K dielectric layer 120 in the trench andon top of the interlayer dielectric layer 128. The bottom of each trench138 is the top surface of the corresponding upper most semiconductornanosheet 111.

As set forth previously, at the point in processing shown in FIG. 1A,the transistor 102, the transistor 104, and the transistor 106 haveidentical structures. In subsequent processing steps, differentiationswill be made in forming the gate metals of each of the transistors 102,104, and 106.

The bottom of each trench 138 is the top surface of the correspondingupper most semiconductor nanosheet 111.

In FIG. 1B a mask 140 has been formed on the first gate metal layer 118of the transistor 106. The mask fills the trench 138 of the transistor106. The mask 140 is patterned so that the mask is not present on thefirst gate metal layer 118 of the transistor 102 and the transistor 104.Accordingly, the first gate metal 118 is exposed for the transistor 102and the transistor 104. The mask 140 can include patterned photo resist.The patterned photoresist can be implemented using standardphotolithography processes.

In FIG. 1C, the first gate metal 118 has been removed at the transistor102 and the transistor 104. Of note, the first gate metal 118 is removedin both the trenches 138 and from the gate structures 114 of thetransistor 102 and the transistor 104. One result of this is that thevoids 116 of the gate structures 114 of the transistor 102 and thetransistor 104 are larger than the voids 116 of the gate structures 114of the transistor 106 in which the first gate metal 118 is stillpresent. The first gate metal 118 can be removed by a wet etch, dryetch, or any other suitable etching process.

In FIG. 1D, the photoresist mask 140 has been removed from thetransistor 106. Accordingly, the first gate metal 118 is exposed at thetransistor 106. The photoresist mask 140 can be removed by any standardphoto resist removal process.

In FIG. 1E, the first gate metal 118 has been etched back within thetrench 138 of the transistor 106. The first gate metal 118 has not beenentirely removed from the trench 138 of the transistor 106. Instead, thefirst gate metal 118 has been etched back with a controlled etch that isselected to leave a remaining portion 142 of the first gate metal 118 onthe high-K dielectric layer 120 at the bottom of the trench 138 of thetransistor 106 while removing the first gate metal 118 from the high-Kdielectric layer 120 on the sidewalls of the trench 138. Accordingly, inone example, the first gate metal 118 is removed from the sidewalls ofthe trench 138 while leaving a remaining portion 142 at the bottom ofthe trench 138.

In one embodiment, the controlled etching process for etching back thefirst gate metal 118 is an atomic layer etching (ALE) process. In oneembodiment, the ALE process is used to etch the titanium nitride firstgate metal 118 to produce the structure shown in transistor 106 of FIG.1E. An ALE process is similar to an atomic layer deposition process(ALD). In an atomic layer etching process, different gases, fluids, ormaterials are flowed into the process chamber for selected periods oftime. Each cycle of an ALE process includes flowing multiple materialsat different stages. Each cycle can result in the removal of an atomicor molecular layer of the titanium nitride first gate metal 118.

In one example, an ALE cycle includes flowing WCI5 into the processchamber for a selected period of time, for example between 1 s and 10 s.The ALE cycle then includes a purge phase in which argon gas is flowedinto the process chamber for a selected period of time, for examplebetween 6 s and 15 s. The ALE cycle then includes flowing O₂ into theprocessing chamber for a selected amount of time, for example between 1s and 10 s. The ALE cycle then includes a second purge phase in whichargon gas is flowed into the process chamber for selected period oftime, for example between 2 s and 15 s. Each cycle results in theremoval of an atomic or molecular layer of the titanium nitride firstgate metal 118. By controlling the number of cycles in an ALE process,the amount of the titanium nitride first gate metal 118 to be etched canbe tightly controlled. Other ALE processes, cycles, durations, andmaterials can be utilized without departing from the scope of thepresent disclosure.

As will be described in more detail below, machine learning processesare utilized to dynamically select parameters for the ALE process. Themachine learning process trains an analysis model to dynamically selectthe parameters for each ALE process. The analysis model can selectmaterials, flow durations, flow pressures, temperatures, and otherparameters associated with ALE processes in order to remove the desiredamount of the titanium nitride first gate metal. Although thisdescription describes an example in which the first gate metal 118 istitanium nitride, the first gate metal 118 can include other materialsthan titanium nitride without departing from the scope of the presentdisclosure.

In FIG. 1F, a second gate metal 144 has been deposited on the integratedcircuit 100. In particular, the second gate metal 144 is deposited onthe high-K dielectric layer 120 in the trenches 138 and in the voids 116of the transistor 102, the transistor 104, 106. The second gate metal144 is deposited on the remaining portion 142 of the first gate metal atthe bottom of the trench 138 and on the first gate metal 118 within thevoids 116 of the transistor 106. The second gate metal 144 can bedeposited by a PVD process, a CVD process, or an ALD process. The secondgate metal includes titanium nitride, in one example. Alternatively, thesecond gate metal 144 can include one or more of polysilicon, aluminum,copper, titanium, tantalum, tungsten, cobalt, molybdenum, tantalumnitride, nickel silicide, cobalt silicide, titanium nitride, WN, TiAl,TiAlN, TaCN, TaC, TaSiN, metal alloys, or other types of conductivematerial. The second gate metal 144 can have a thickness between 1 nmand 5 nm. Other materials, deposition processes, and thicknesses can beutilized for the second gate metal 144 without departing from the scopeof the present disclosure.

In FIG. 1G a mask 146 has been formed on the second gate metal 144 ofthe transistor 104 and the transistor 106. The mask 146 fills thetrenches 138 of the transistor 104 and the transistor 106. The mask 146is patterned so that the mask is not present on the second gate metallayer 144 of the transistor 102. Accordingly, the second gate metallayer 144 is exposed for the transistor 102. The mask 140 can includepatterned photo resist. The patterned photoresist can be implementedusing standard photolithography processes.

In FIG. 1H, the second gate metal 144 has been removed at the transistor102. Of note, the second gate metal 144 is removed in both the trench138 and from the gate structures 114 of the transistor 102. One resultof this is that the voids 116 of the gate structures 114 of thetransistor 102 is larger than the voids 116 of the gate structures 114of the transistor 104 in which the second gate metal 144 is stillpresent. The voids 116 of the gate structures 114 of the transistor 104are larger than the voids 116 of the gate structures 114 of thetransistor 106 in which the first gate metal 118 and the second gatemetal 144 are still present. The second gate metal 144 can be removed bya wet etch, dry etch, or any other suitable etching process.

In FIG. 1H, the photoresist mask 146 has been removed from thetransistor 104 and the transistor 106. Accordingly, the second gatemetal 144 is exposed at the transistor 104 and the transistor 106. Thephotoresist mask 146 can be removed by any standard photo resist removalprocess.

In FIG. 1I, the second gate metal 144 has been etched back within thetrenches 138 of the transistor 104 and the transistor 106. The secondgate metal 144 has not been entirely removed from the trenches 138 ofthe transistor 104 and the transistor 106. Instead, the second gatemetal 144 has been etched back with a controlled etch that is selectedto leave a remaining portion 150 of the second gate metal 144 at thebottom of the trenches of the transistor 104 and the transistor 106. Inthe transistor 104, the remaining portion 150 of the second gate metal144 is positioned on the high-K dielectric layer 120 at the bottom ofthe trench 138. In the transistor 106, the remaining portion 150 of thesecond gate metal 144 is positioned on the remaining portion 142 of thefirst gate metal 118. The remaining portion 150 may extend vertically ona portion of high-K dielectric layer 120 in the trenches 138 in thetransistor 104 and the transistor 106.

The controlled etch that etches back the second gate metal 144 is thesame type of controlled etch as the process that etches back the firstgate metal 118 as described in relation to FIG. 1E in particular, an ALEprocess with dynamically controllable characteristics can be utilized toetch back the second gate metal 144. The controlled etch can becontrolled by an analysis model of a control system. The analysis modelcan be trained with a machine learning process to select the parametersof the ALE process based on the desired characteristics of the remainingportion 150 of the second gate metal 144.

In FIG. 1J a third gate metal 152 has been deposited on the high-Kdielectric layer 120 in the trenches of the transistor 102, thetransistor 104, and the transistor 106. The third gate metal 152 ispositioned on the remaining portions 150 of the second gate metal 144 inthe trenches 138 of the transistor 104 and the transistor 106. The thirdgate metal 152 is positioned in the voids 116 of the gate structures 114of the transistor 102, the transistor 104, and the transistor 106.

The third gate metal 152 can be deposited by a PVD process, a CVDprocess, or an ALD process. The third gate metal 152 can include one ormore of polysilicon, aluminum, copper, titanium, tantalum, tungsten,cobalt, molybdenum, tantalum nitride, nickel silicide, cobalt silicide,titanium nitride, WN, TiAl, TiAlN, TaCN, TaC, TaSiN, metal alloys, orother types of conductive material. The third gate metal 152 can have athickness between 1 nm and 5 nm. Other materials, deposition processes,and thicknesses can be utilized for the third gate metal 152 withoutdeparting from the scope of the present disclosure.

In FIG. 1J, a titanium nitride layer 154 has been formed on the thirdgate metal 152 in the trenches 138 of the transistor 102, the transistor104, and the transistor 106. The titanium nitride layer 154 is alsopositioned in the voids 116 of the gate structures 114 of the transistor102 and the transistor 104. In one embodiment, the titanium nitridelayer 154 is not positioned in the voids 116 of the gate structures 114of the transistor 106 because the void has been entirely filled by apreviously deposited layers. However, in some cases, the voids 116 ofthe gate structures 114 of the transistor 106 may not have been entirelyfilled by previously deposited layers. In this case, the titaniumnitride layer 154 may also be present in the voids 116 of the gatestructures 114 of the transistor 106.

In FIG. 1J, an in-situ silane passivation layer 156 has been formed onthe titanium nitride layer 154 and the trenches 138 of the transistor102, the transistor 104, and the transistor 106. The in-situ silanepassivation layer 156 is also present in the gate structures 116 of thetransistor 102. In the example FIG. 1J, the in-situ silane passivationlayer 156 is not present in the voids 116 of the gate structures of thetransistor 104 and the transistor 106 because the voids 116 have beenentirely filled by previously deposited layers. However, it is possiblethat the in-situ silane passivation layer 156 can be present in thevoids 116 of the gate structure of the transistor 104 and the transistor106 depending on the size of the voids 116 and a thickness of previouslydeposited layers.

In FIG. 1K, a titanium nitride glue layer 158 has been formed on thein-situ silane passivation layer 156 in the trenches 138 of thetransistor 102, the transistor 104, and the transistor 106. The titaniumnitride glue layer 158 can be deposited and can have thicknesses andcharacteristics described in relation to previously described titaniumnitride layers.

In FIG. 1K a fourth gate metal layer 160 has been deposited on thetitanium nitride glue layer 158 in the trenches 138 of the transistor102, the transistor 104, and the transistor 106. The fourth gate metallayer 160 fills the remaining portion of the trenches 138. In practice,the fourth gate metal layer 160 may fill a much larger portion of thetrenches 138 as shown in FIG. 1K. The fourth gate metal 160 is aconductive gate fill material. While FIG. 1K illustrates the fourth gatemetal layer 160 filling a relatively small portion of the trench 138, inpractice the fourth gate metal 160 may fill a large majority of thetrench 138. The thicknesses of the other layers in the trenches 138 maybe very small compared to the widths of the trenches 138, though this isdifficult to illustrate when showing a large number of distinct layers.Accordingly, the fourth gate metal layer 160 may fill up a large portionof the volume of the trenches 138.

In one embodiment, the fourth gate metal layer 160 is tungsten depositedby a CVD process. Alternatively, the fourth gate metal layer can includeone or more of polysilicon, aluminum, copper, titanium, tantalum,tungsten, cobalt, molybdenum, WN, TiAl, TiAlN, TaCN, TaC, TaSiN, metalalloys, or other types of conductive material. The fourth gate metallayer 160 can be formed by a PVD process, an ALD process, or by otherdeposition processes. Other materials and processes can be utilized forthe fourth gate metal 160 without departing from the scope of thepresent disclosure. Though not shown in FIG. 1K, the gate structures 114of the transistor 102, the transistor 104, and the transistor 106 mayinclude the fourth gate metal 160 if there were remaining portions ofthe corresponding voids 116 at the time of the deposition of the fourthgate metal 160.

In FIG. 1L, a chemical mechanical planarization (CMP) process has beenperformed. The CMP process removes the high-K dielectric material 120,the third gate metal 152, the titanium nitride layer 154, the in-situsilane passivation layer 156, the titanium nitride layer 158, and thefourth gate metal 160 outside of the trenches 138. The CMP process alsoremoves portions of the first sidewall spacers 134 and the interlayerdielectric layer 128.

The remaining portions of the third gate metal 152, the titanium nitridelayer 154, the in-situ silane passivation layer 156, the titaniumnitride layer 158, and the fourth gate metal 160 correspond to the gateelectrode 162 of the transistor 102, the transistor 104, and thetransistor 106. The gate electrode 162 of the transistor 104 alsoincludes the remaining portion 150 of the second gate metal 144. Thegate electrode 162 of the transistor 106 includes the remaining portion142 of the first gate metal 118 and the remaining portion 150 of thesecond gate metal 144.

The conductive layers in the gate structures 114 are part of the gateelectrodes 162. The gate electrodes 162 surround the semiconductornanosheets 111 and can render the channel regions corresponding to thesemiconductor nanosheets 111 conductive or nonconductive by applicationof voltages to the gate electrodes.

The presence of the remaining portion 150 of the second gate metal 144and the gate electrode 162 of the transistor 104 results in thetransistor 104 having a higher threshold voltage than the thresholdvoltage of the transistor 102. The presence of the remaining portion 142of the first gate metal 118 and the remaining portion 150 of the secondgate metal 144 in the transistor 106 results in the transistor 106having a higher threshold voltage than the transistor 104. This can beaccomplished due to the controlled etching process that etches back thefirst gate metal 118 without entirely removing the first gate metal 118from the trench 138 of the transistor 106, and due to the controlledetching process that etches back the second gate metal 144 withoutentirely removing the second gate metal 144 from the trenches 138 of thetransistor 104 and the transistor 106.

Due to the controlled etching processes that define the remainingportions 142 and 150 of the first and second gatemetals 118 and 144, thefourth gate metal 160 extends to a higher vertical level within thetrench 138 than do the remaining portions 142 and 150 of the first andsecond gate metals 118 and 144 in the trench 138 of the transistor 106.The fourth gate metal 160 extends to the top of the trench 138.Similarly, the fourth gate metal 160 extends to a higher vertical levelwithin the trench 138 of the transistor 104 than does the remainingportion 150 of the second gate metal 144. This may be one characteristicof the controlled etching processes that enable the formation oftransistors having different threshold voltages.

The presence of the remaining portions 142 and 150 of the first andsecond gate metals in the transistor 106 results in the bottom of thefourth gate metal 160 being farther from the bottom of the trench 138 ofthe transistor 106 than the fourth gate metal 160 of the transistor 104is from the bottom of the trench 138 of the transistor 104. Thiscontributes to the higher threshold voltage of the transistor 106. Partof the reason for this is that the first and second gate metals 118, 144have a higher work function than the fourth gate metal 160.

The presence of the remaining portion 150 of the second gate metal inthe transistor 104 results in the bottom of the fourth gate metal 160being farther from the bottom of the trench 138 of the transistor 104than the fourth gate metal 160 of the transistor 102 is from the bottomof the trench 138 of the transistor 102. This contributes to the higherthreshold voltage of the transistor 104 due, in part, to the second gatemetal 144 having a higher work function than the fourth gate metal 160.

In FIG. 1M, silicide layers 166 have been formed in the source and drainregions 112 of each of the transistors 102, 104, and 106. The silicidelayers 166 can include titanium silicide, cobalt silicide, or othertypes of silicide. In FIG. 1M, cobalt contact plugs 168 have been formedin the interlayer dielectric layer 128 in each of the transistors 102,104, and 108. The cobalt contact plugs 160 can be utilized to applyvoltages to the source and drain regions 112 of the transistors 102,104, and 106. The plugs 160 and the silicide layers 166 can includeother materials without departing from the scope of the presentdisclosure.

FIGS. 1A-1M describe the formation of N-channel transistors. A P-channelultra-low threshold voltage transistor, a P-channel low thresholdvoltage transistor, and a P-channel standard threshold voltagetransistor can be formed during the same process. One difference is thatin the regions of the integrated circuit 100 where P-channel transistorswill be formed, the semiconductor materials will be doped differently.For example, the source and drain regions 112 of P-channel transistorswill be doped with P-type dopants. The semiconductor substrate 110 andthe semiconductor nanosheets 111 may also be doped in accordance withthe process for doping P-channel transistors. For P-channel transistors,the mask 140 shown in FIG. 1B will fill the trench associated with theultra-low threshold voltage P-channel transistor, eventually resultingin the remaining portion 142 of the first gate metal 118 being found inthe trench 138 of the ultra-low threshold voltage P-channel transistorswhile the first gate metal 118 will be entirely removed from the lowthreshold voltage and standard threshold voltage P-channel transistors.For P-channel transistors, the mask 146 shown in FIG. 1G will fill thetrenches 138 associated with the ultra-low threshold voltage P-channeltransistor and the low-voltage P-channel transistor, eventuallyresulting in the remaining portion 150 of the second gate metal 144 beenfound in the trenches 138 of the ultra-low threshold voltage P-channeltransistor and the low-voltage P-channel transistors. Accordingly, theprocess shown in FIGS. 1A-1M will simultaneously form ultra-low-voltage,low-voltage, and standard threshold voltage N-channel transistors andP-channel transistors, according to one embodiment.

FIG. 2A is an illustration of a semiconductor process system 200,according to one embodiment. The semiconductor process system 200includes a process chamber 202 including an interior volume 203. Asupport 206 is positioned within the interior volume 203 and isconfigured to support a substrate 204 during a thin-film etchingprocess. The semiconductor process system 200 is configured to etch athin film on the substrate 204. The semiconductor process system 200includes a control system 224 that dynamically adjusts thin-film etchingparameters. Details of the control system 224 are provided afterdescription of the operation of the semiconductor process system 200.

In one embodiment, the semiconductor process system 200 includes a firstfluid source 208 and a second fluid source 210. The first fluid source208 supplies a first fluid into the interior volume 203. The secondfluid source 210 supplies a second fluid into the interior volume 203.The first and second fluids both contribute in etching a thin film onthe substrate 204. While FIG. 2A illustrates fluid sources 208 and 210,in practice, the fluid sources 208 and 210 may include or supplymaterials other than fluids. For example, the fluid sources 208 and 210may include material sources that provide all materials for the etchingprocess.

In one embodiment, the semiconductor process system 200 is an atomiclayer etching (ALE) system that performs ALE processes. The ALE systemperforms etching processes in cycles. Each cycle includes flowing afirst etching fluid from the fluid source 208, followed by purging thefirst etching fluid from the etching chamber by flowing the purge gasfrom one or both of the purge sources 212 and 224, followed by flowing asecond etching fluid from the fluid source 210, followed by purging thesecond etching fluid from the etching chamber by flowing the purge gasfrom one or both of the purge sources 212 and 224. This corresponds to asingle ALE cycle. Each cycle etches an atomic or molecular layer fromthe thin-film that is being etched.

The parameters of a thin film generated by the semiconductor processsystem 200 can be affected by large number of process conditions. Theprocess conditions can include, but are not limited to, an amount offluid or material remaining in the fluid sources 208, 210, a flow rateof fluid or material from the fluid sources 208, 210, the pressure offluids provided by the fluid sources 208 and 210, the length of tubes orconduits that carry fluid or material into the process chamber 202, theage of an ampoule defining or included in the process chamber 202, thetemperature within the process chamber 202, the humidity within theprocess chamber 202, the pressure within the process chamber 202, lightabsorption and reflection within the process chamber 202, surfacefeatures of the semiconductor wafer 204, the composition of materialsprovided by the fluid sources 208 and 210, the phase of materialsprovided by the fluid sources 208 and 210, the duration of the etchingprocess, the duration of individual phases of the etching process, andvarious other factors, including factors not specifically listed above.

The combination of the various process conditions during the etchingprocess determines the remaining thickness of a thin film etched by theALE process. It is possible that process conditions may result in thinfilms that do not have remaining thicknesses that fall within targetparameters. If this happens, then integrated circuits formed from thesemiconductor wafer 204 may not function properly. The quality ofbatches of semiconductor wafers may suffer. In some cases, somesemiconductor wafers may need to be scrapped.

The semiconductor process system 200 utilizes the control system 224 todynamically adjust process conditions to ensure that etching processesresult in thin films having parameters or characteristics that fallwithin target parameters or characteristics. The control system 224 isconnected to processing equipment associated with the semiconductorprocess system 200. The processing equipment can include componentsshown in FIG. 2A. The control system 224 can control the flow rate ofmaterial from the fluid sources 208 and 210, the temperature ofmaterials supplied by the fluid sources 208 and 210, the pressure offluids provided by the fluid sources 208 and 210, the flow rate ofmaterial from purge sources 212 and 214, the duration of flow ofmaterials from the fluid sources 208 and 210 and the purge sources 212and 214, the temperature within the process chamber 202, the pressurewithin the process chamber 202, the humidity within the process chamber202, and other aspects of the thin-film etching process. The controlsystem 224 controls these process parameters so that the thin-filmetching process results in a thin-film having target parameters such asa target remaining thickness, a target composition, a target crystalorientation, etc. Further details regarding the control system areprovided in relation to FIGS. 7-9 .

In one embodiment, the control system 224 is communicatively coupled tothe first and second fluid sources 208, 210 via one or morecommunication channels 225. The control system 224 can send signals tothe first fluid source 208 and the second fluid source 210 via thecommunication channels 225. The control system 224 can controlfunctionality of the first and second fluid sources 208, 210 responsive,in part, to the sensor signals from the byproduct sensor 222.

In one embodiment, the semiconductor process system 200 can include oneor more valves, pumps, or other flow control mechanisms for controllingthe flow rate of the first fluid from the first fluid source 208. Theseflow control mechanisms may be part of the fluid source 208 or may beseparate from the fluid source 208. The control system 224 can becommunicatively coupled to these flow control mechanisms or to systemsthat control these flow control mechanisms. The control system 224 cancontrol the flowrate of the first fluid by controlling these mechanisms.The control system 200 may include valves, pumps, or other flow controlmechanisms that control the flow of the second fluid from the secondfluid source 210 in the same manner as described above in reference tothe first fluid and the first fluid source 208.

In one embodiment, the semiconductor process system 200 includes amanifold mixer 216 and a fluid distributor 218. The manifold mixer 216receives the first and second fluids, either together or separately,from the first fluid source 208 and the second fluid source 210. Themanifold mixer 216 provides either the first fluid, the second fluid, ora mixture of the first and second fluids to the fluid distributor 218.The fluid distributor 218 receives one or more fluids from the manifoldmixer 216 and distributes the one or more fluids into the interiorvolume 203 of the process chamber 202.

In one embodiment, the first fluid source 208 is coupled to the manifoldmixer 216 by a first fluid channel 230. The first fluid channel 230carries the first fluid from the fluid source 208 to the manifold mixer216. The first fluid channel 230 can be a tube, pipe, or other suitablechannel for passing the first fluid from the first fluid source 208 tothe manifold mixer 216. The second fluid source 210 is coupled to themanifold mixer 216 by second fluid channel 232. The second fluid channel232 carries the second fluid from the second fluid source 210 to themanifold mixer 216.

In one embodiment, the manifold mixer 216 is coupled to the fluiddistributor 218 by a third fluid line 234. The third fluid line 234carries fluid from the manifold mixer 216 to the fluid distributor 218.The third fluid line 234 may carry the first fluid, the second fluid, amixture of the first and second fluids, or other fluids, as will bedescribed in more detail below.

The first and second fluid sources 208, 210 can include fluid tanks. Thefluid tanks can store the first and second fluids. The fluid tanks canselectively output the first and second fluids.

In one embodiment, the semiconductor process system 200 includes a firstpurge source 212 and the second purge source 214. The first purge sourceis coupled to the first fluid line 230 by first purge line 236. Thesecond purge source is coupled to the fluid line 232 by second purgeline 238. In practice, the first and second purge sources may be asingle purge source.

In one embodiment, the first and second purge sources 212, 214 supply apurging gas into the interior volume 203 of the process chamber 202. Thepurge fluid is a fluid selected to purge or carry the first fluid, thesecond fluid, byproducts of the first or second fluid, or other fluidsfrom the interior volume 203 of the process chamber 202. The purge fluidis selected to not react with the substrate 204, the gate metal layer onthe substrate 204, the first and second fluids, and byproducts of thisfirst or second fluid. Accordingly, the purge fluid may be an inert gasincluding, but not limited to, Ar or N2.

While FIG. 2A illustrates a first fluid source 208 and a second fluidsource 210, in practice the semiconductor process system 200 can includeother numbers of fluid sources. For example, the semiconductor processsystem 200 may include only a single fluid source or more than two fluidsources. Accordingly, the semiconductor process system 200 can include adifferent number than two fluid sources without departing from the scopeof the present disclosure.

FIG. 2B is a graph illustrating a cycle of an ALE process, according toone embodiment. At time T1 the first etching fluid begins to flow. Inthe example of FIG. 2B, the first etching fluid is WCI5. The firstetching fluid flows from the fluid source 208 into the interior volume203. In the interior volume 203, the first etching fluid reacts with thetop expose layer of the titanium nitride layer 154. At time T2, thefirst etching fluid WCI5 stops flowing. In one example, the time elapsedbetween T1 and T2 is between 1 s and 10 s.

At time T3, the purge gas begins to flow. The purge gas flows from oneor both of the purge sources 212 and 224. In one example, the purge gasis one of argon, N2, or another inert gas that can purge the firstetching fluid WCI5 without reacting with the titanium nitride layer 154.At time T4, the purge gas stops flowing. In one example, the timeelapsed between T3 and T4 is between 2 s and 15 s.

At time T5, the second etching fluid flows into the interior volume 203.The second etching fluid flows from the fluid source 210 into theinterior volume 203. In one example, the second etching fluid is O₂. TheO₂ reacts with the top atomic or molecular layer of the titanium nitridelayer 154 and completes the etching of the top atomic or molecular layerof the titanium nitride layer 154. At time T6, the second etching fluidstops flowing. In one example, the elapsed time between T5 and T6 isbetween 1 s and 10 s.

At time T7, the purge gas flows again and purges the interior volume 203of the second etching fluid. At time T8 the purge gas stops flowing. Thetime between T1 and T8 corresponds to a single ALE cycle.

In practice, an ALE process may include between 5 and 50 cycles,depending on the initial thickness of the titanium nitride layer and thedesired final thickness of the titanium nitride layer. Each cycleremoves an atomic or molecular layer of the titanium nitride layer 154.Other materials, processes, and elapsed times can be utilized withoutdeparting from the scope of the present disclosure.

FIG. 3A is a block diagram of the control system 224 of FIG. 2A,according to one embodiment. The control system 224 of FIG. 3A isconfigured to control operation of a semiconductor process system 200,according to one embodiment. Accordingly, the control system 224 can beutilized in conjunction with processes, systems, and structuresdisclosed in relation to FIGS. 1A-2B. The control system 224 utilizesmachine learning to adjust parameters of the semiconductor processsystem 200. The control system 224 can adjust parameters of thesemiconductor process system 200 between ALE runs or even between ALEcycles in order to ensure that a thin-film layer formed by the ALEprocess falls within selected specifications.

In one embodiment, the control system 224 includes an analysis model 302and a training module 304. The training module trains the analysis model302 with a machine learning process. The machine learning process trainsthe analysis model 302 to select parameters for an ALE process that willresult in a thin film having selected characteristics. Although thetraining module 304 is shown as being separate from the analysis model302, in practice, the training module 304 may be part of the analysismodel 302.

The control system 224 includes, or stores, training set data 306. Thetraining set data 306 includes historical thin-film data 308 andhistorical process conditions data 310. The historical thin-film data308 includes data related to thin films resulting from ALE processes.The historical process conditions data 310 includes data related toprocess conditions during the ALE processes that generated the thinfilms. As will be set forth in more detail below, the training module304 utilizes the historical thin-film data 308 and the historicalprocess conditions data 310 to train the analysis model 302 with amachine learning process.

In one embodiment, the historical thin-film data 308 includes datarelated to the remaining thickness of previously etched thin films. Forexample, during operation of a semiconductor fabrication facility,thousands or millions of semiconductor wafers may be processed over thecourse of several months or years. Each of the semiconductor wafers mayinclude thin films etched by ALE processes. After each ALE process, thethicknesses of the thin-films are measured as part of a quality controlprocess. The historical thin-film data 308 includes the remainingthicknesses of each of the thin films etched by ALE processes.Accordingly, the historical thin-film data 308 can include thicknessdata for a large number of thin-films etched by ALE processes.

In one embodiment, the historical thin-film data 308 may also includedata related to the thickness of thin films at intermediate stages ofthe thin-film etching processes. For example, an ALE process may includea large number of etching cycles during which individual layers of thethin film are etched. The historical thin-film data 308 can includethickness data for thin films after individual etching cycles or groupsof etching cycles. Thus, the historical thin-film data 308 not onlyincludes data related to the total thickness of a thin film aftercompletion of an ALE process, but may also include data related to thethickness of the thin film at various stages of the ALE process.

In one embodiment, the historical thin-film data 308 includes datarelated to the composition of the remaining thin films etched by ALEprocesses. After a thin film is etched, measurements can be made todetermine the elemental or molecular composition of the thin films.Successful etching of the thin films results in a thin film thatincludes particular remaining thicknesses. Unsuccessful etchingprocesses may result in a thin film that does not include the specifiedproportions of elements or compounds. The historical thin-film data 308can include data from measurements indicating the elements or compoundsthat make up the various thin films.

In one embodiment, the historical process conditions 310 include variousprocess conditions or parameters during ALE processes that etch the thinfilms associated with the historical thin-film data 308. Accordingly,for each thin film having data in the historical thin-film data 308, thehistorical process conditions data 310 can include the processconditions or parameters that were present during etching of the thinfilm. For example, the historical process conditions data 310 caninclude data related to the pressure, temperature, and fluid flow rateswithin the process chamber during ALE processes.

The historical process conditions data 310 can include data related toremaining amounts of precursor material in the fluid sources during ALEprocesses. The historical process conditions data 310 can include datarelated to the age of the process chamber 202, the number of etchingprocesses that have been performed in the process chamber 202, a numberof etching processes that have been performed in the process chamber 202since the most recent cleaning cycle of the process chamber 202, orother data related to the process chamber 202. The historical processconditions data 310 can include data related to compounds or fluidsintroduced into the process chamber 202 during the etching process. Thedata related to the compounds can include types of compounds, phases ofcompounds (solid, gas, or liquid), mixtures of compounds, or otheraspects related to compounds or fluids introduced into the processchamber 202. The historical process conditions data 310 can include datarelated to the humidity within the process chamber 202 during ALEprocesses. The historical process conditions data 310 can include datarelated to light absorption, light adsorption, and light reflectionrelated to the process chamber 202. The historical process conditionsdata 310 can include data related to the length of pipes, tubes, orconduits that carry compounds or fluids into the process chamber 202during ALE processes. The historical process conditions data 310 caninclude data related to the condition of carrier gases that carrycompounds or fluids into the process chamber 202 during ALE processes.

In one embodiment, historical process conditions data 310 can includeprocess conditions for each of a plurality of individual cycles of asingle ALE process. Accordingly, the historical process conditions data310 can include process conditions data for a very large number of ALEcycles.

In one embodiment, the training set data 306 links the historicalthin-film data 308 with the historical process conditions data 310. Inother words, the thin-film thickness, material composition, or crystalstructure associated with a thin film in the historical thin-film data308 is linked to the process conditions data associated with thatetching process. As will be set forth in more detail below, the labeledtraining set data can be utilized in a machine learning process to trainthe analysis model 302 to predict semiconductor process conditions thatwill result in properly formed thin films.

In one embodiment, the control system 324 includes processing resources312, memory resources 314, and communication resources 316. Theprocessing resources 312 can include one or more controllers orprocessors. The processing resources 312 are configured to executesoftware instructions, process data, make thin-film etching controldecisions, perform signal processing, read data from memory, write datato memory, and to perform other processing operations. The processingresources 312 can include physical processing resources 312 located at asite or facility of the semiconductor process system 200. The processingresources can include virtual processing resources 312 remote from thesite semiconductor process system 200 or a facility at which thesemiconductor process system 200 is located. The processing resources312 can include cloud-based processing resources including processorsand servers accessed via one or more cloud computing platforms.

In one embodiment, the memory resources 314 can include one or morecomputer readable memories. The memory resources 314 are configured tostore software instructions associated with the function of the controlsystem and its components, including, but not limited to, the analysismodel 302. The memory resources 314 can store data associated with thefunction of the control system 224 and its components. The data caninclude the training set data 306, current process conditions data, andany other data associated with the operation of the control system 224or any of its components. The memory resources 314 can include physicalmemory resources located at the site or facility of the semiconductorprocess system 200. The memory resources can include virtual memoryresources located remotely from site or facility of the semiconductorprocess system 200. The memory resources 314 can include cloud-basedmemory resources accessed via one or more cloud computing platforms.

In one embodiment, the communication resources can include resourcesthat enable the control system 224 to communicate with equipmentassociated with the semiconductor process system 200. For example, thecommunication resources 316 can include wired and wireless communicationresources that enable the control system 224 to receive the sensor dataassociated with the semiconductor process system 200 and to controlequipment of the semiconductor process system 200. The communicationresources 316 can enable the control system 224 to control the flow offluids or other material from the fluid sources 208 and 210 and from thepurge sources 212 and 214. The communication resources 316 can enablethe control system 224 to control heaters, voltage sources, valves,exhaust channels, wafer transfer equipment, and any other equipmentassociated with the semiconductor process system 200. The communicationresources 316 can enable the control system 224 to communicate withremote systems. The communication resources 316 can include, or canfacilitate communication via, one or more networks such as wirenetworks, wireless networks, the Internet, or an intranet. Thecommunication resources 316 can enable components of the control system224 to communicate with each other.

In one embodiment, the analysis model 302 is implemented via theprocessing resources 312, the memory resources 314, and thecommunication resources 316. The control system 224 can be a dispersedcontrol system with components and resources and locations remote fromeach other and from the semiconductor process system 200.

FIG. 3B is a block diagram 350 illustrating operational aspects andtraining aspects of analysis model 302, according to one embodiment. Asdescribed previously, the training set data 306 includes data related toa plurality of previously performed thin-film etching processes. Eachpreviously performed thin-film etching process took place withparticular process conditions and resulted in a thin-film having aparticular characteristics. The process conditions for each previouslyperformed thin-film etching process are formatted into a respectiveprocess conditions vector 352. The process conditions vector includes aplurality of data fields 354. Each data field 354 corresponds to aparticular process condition.

The example of FIG. 3B illustrates a single process conditions vector352 that will be passed to the analysis model 302 during the trainingprocess. In the example of FIG. 3B, the process conditions vector 352includes nine data fields 354. A first data field 354 corresponds to thetemperature during the previously performed thin-film etching process. Asecond data field 356 corresponds to the pressure during the previouslyperformed thin-film etching process. A third data field 354 correspondsto the humidity during the previously performed thin-film etchingprocess. The fourth data field 354 corresponds to the flow rate ofetching materials during the previously performed thin-film etchingprocess. The fifth data field 354 corresponds to the phase (liquid,solid, or gas) of etching materials during the previously performedthin-film etching process. The sixth data field 354 corresponds to theage of the ampoule used in the previously performed thin-film etchingprocess. The seventh data field 354 corresponds to a size of an etchingarea on a wafer during the previously performed thin-film etchingprocess. The eighth data field 354 corresponds to the density of surfacefeatures of the wafer utilized during the previously performed thin-filmetching process. The ninth data field corresponds to the angle ofsidewalls of surface features during the previously performed thin-filmetching process. In practice, each process conditions vector 352 caninclude more or fewer data fields than are shown in FIG. 3B withoutdeparting from the scope of the present disclosure. Each processconditions vector 352 can include different types of process conditionswithout departing from the scope of the present disclosure. Theparticular process conditions illustrated in FIG. 3B are given only byway of example. Each process condition is represented by a numericalvalue in the corresponding data field 354. For condition types that arenot naturally represented in numbers, such as material phase, a numbercan be assigned to each possible phase.

The analysis model 302 includes a plurality of neural layers 356 a-e.Each neural layer includes a plurality of nodes 358. Each node 358 canalso be called a neuron. Each node 358 from the first neural layer 356 areceives the data values for each data field from the process conditionsvector 352. Accordingly, in the example of FIG. 3B, each node 358 fromthe first neural layer 356 a receives nine data values because theprocess conditions vector 352 has nine data fields. Each neuron 358includes a respective internal mathematical function labeled F(x) inFIG. 3B. Each node 358 of the first neural layer 356 a generates ascalar value by applying the internal mathematical function F(x) to thedata values from the data fields 354 of the process conditions vector352. Further details regarding the internal mathematical functions F(x)are provided below.

Each node 358 of the second neural layer 356 b receives the scalarvalues generated by each node 358 of the first neural layer 356 a.Accordingly, in the example of FIG. 3B each node of the second neurallayer 356 b receives four scalar values because there are four nodes 358in the first neural layer 356 a. Each node 358 of the second neurallayer 356 b generates a scalar value by applying the respective internalmathematical function F(x) to the scalar values from the first neurallayer 356 a.

Each node 358 of the third neural layer 356 c receives the scalar valuesgenerated by each node 358 of the second neural layer 356 b.Accordingly, in the example of FIG. 3B each node of the third neurallayer 356 c receives five scalar values because there are five nodes 358in the second neural layer 356 b. Each node 358 of the third neurallayer 356 c generates a scalar value by applying the respective internalmathematical function F(x) to the scalar values from the nodes 358 ofthe second neural layer 356 b.

Each node 358 of the neural layer 356 d receives the scalar valuesgenerated by each node 358 of the previous neural layer (not shown).Each node 358 of the neural layer 356 d generates a scalar value byapplying the respective internal mathematical function F(x) to thescalar values from the nodes 358 of the second neural layer 356 b.

The final neural layer includes only a single node 358. The final neurallayer receives the scalar values generated by each node 358 of theprevious neural layer 356 d. The node 358 of the final neural layer 356e generates a data value 368 by applying a mathematical function F(x) tothe scalar values received from the nodes 358 of the neural layer 356 d.

In the example of FIG. 3B, the data value 368 corresponds to thepredicted remaining thickness of a thin film generated by processconditions data corresponding to values included in the processconditions vector 352. In other embodiments, the final neural layer 356e may generate multiple data values each corresponding to a particularthin-film characteristic such as thin-film crystal orientation,thin-film uniformity, or other characteristics of a thin film. The finalneural layer 356 e will include a respective node 358 for each outputdata value to be generated. In the case of a predicted thin filmthickness, engineers can provide constraints that specify that thepredicted thin film thickness 368 must fall within a selected range,such as between 0 nm and 50 nm, in one example. The analysis model 302will adjust internal functions F(x) to ensure that the data value 368corresponding to the predicted thin film thickness will fall within thespecified range.

During the machine learning process, the analysis model compares thepredicted remaining thickness in the data value 368 to the actualremaining thickness of the thin-film as indicated by the data value 370.As set forth previously, the training set data 306 includes, for eachset of historical process conditions data, thin-film characteristicsdata indicating the characteristics of the thin-film that resulted fromthe historical thin-film etching process. Accordingly, the data field370 includes the actual remaining thickness of the thin-film thatresulted from the etching process reflected in the process conditionsvector 352. The analysis model 302 compares the predicted remainingthickness from the data value 368 to the actual remaining thickness fromthe data value 370. The analysis model 302 generates an error value 372indicating the error or difference between the predicted remainingthickness from the data value 368 and the actual remaining thicknessfrom the data value 370. The error value 372 is utilized to train theanalysis model 302.

The training of the analysis model 302 can be more fully understood bydiscussing the internal mathematical functions F(x). While all of thenodes 358 are labeled with an internal mathematical function F(x), themathematical function F(x) of each node is unique. In one example, eachinternal mathematical function has the following form:F(x)=x ₁ *w ₁ +x ₂ *w ₂ + . . . x _(n) *w ₁ +b.

In the equation above, each value x₁-x_(n) corresponds to a data valuereceived from a node 358 in the previous neural layer, or, in the caseof the first neural layer 356 a, each value x₁-x_(n) corresponds to arespective data value from the data fields 354 of the process conditionsvector 352. Accordingly, n for a given node is equal to the number ofnodes in the previous neural layer. The values w₁-w_(n) are scalarweighting values associated with a corresponding node from the previouslayer. The analysis model 302 selects the values of the weighting valuesw₁-w_(n). The constant b is a scalar biasing value and may also bemultiplied by a weighting value. The value generated by a node 358 isbased on the weighting values w₁-w_(n). Accordingly, each node 358 has nweighting values w₁-w_(n). Though not shown above, each function F(x)may also include an activation function. The sum set forth in theequation above is multiplied by the activation function. Examples ofactivation functions can include rectified linear unit (ReLU) functions,sigmoid functions, hyperbolic tension functions, or other types ofactivation functions.

After the error value 372 has been calculated, the analysis model 302adjusts the weighting values w₁-w_(n) for the various nodes 358 of thevarious neural layers 356 a-356 e. After the analysis model 302 adjuststhe weighting values w₁-w_(n), the analysis model 302 again provides theprocess conditions vector 352 to the input neural layer 356 a. Becausethe weighting values are different for the various nodes 358 of theanalysis model 302, the predicted remaining thickness 368 will bedifferent than in the previous iteration. The analysis model 302 againgenerates an error value 372 by comparing the actual remaining thickness370 to the predicted remaining thickness 368.

The analysis model 302 again adjusts the weighting values w₁-w_(n)associated with the various nodes 358. The analysis model 302 againprocesses the process conditions vector 352 and generates a predictedremaining thickness 368 and associated error value 372. The trainingprocess includes adjusting the weighting values w₁-w_(n) in iterationsuntil the error value 372 is minimized.

FIG. 3B illustrates a single process conditions vector 352 being passedto the analysis model 302. In practice, the training process includespassing a large number of process conditions vectors 352 through theanalysis model 302, generating a predicted remaining thickness 368 foreach process conditions vector 352, and generating associated errorvalue 372 for each predicted remaining thickness. The training processcan also include generating an aggregated error value indicating theaverage error for all the predicted remaining thicknesses for a batch ofprocess conditions vectors 352. The analysis model 302 adjusts theweighting values w₁-w_(n) after processing each batch of processconditions vectors 352. The training process continues until the averageerror across all process conditions vectors 352 is less than a selectedthreshold tolerance. When the average error is less than the selectedthreshold tolerance, the analysis model 302 training is complete and theanalysis model is trained to accurately predict the thickness of thinfilms based on the process conditions. The analysis model 302 can thenbe used to predict thin-film thicknesses and to select processconditions that will result in a desired thin-film thickness. During useof the trained model 302, a process conditions vector, representingcurrent process condition for a current thin film etching process to beperformed, and having the same format at the process conditions vector352, is provided to the trained analysis model 302. The trained analysismodel 302 can then predict the thickness of a thin film that will resultfrom those process conditions.

A particular example of a neural network based analysis model 302 hasbeen described in relation to FIG. 3B. However, other types of neuralnetwork based analysis models, or analysis models of types other thanneural networks can be utilized without departing from the scope of thepresent disclosure. Furthermore, the neural network can have differentnumbers of neural layers having different numbers of nodes withoutdeparting from the scope of the present disclosure.

FIG. 4 is a flow diagram of a process 400 for training an analysis modelto identify process conditions that will result in proper etching of athin film, according to one embodiment. One example of an analysis modelis the analysis model 302 of FIG. 3A. The various steps of the process400 can utilize components, processes, and techniques described inrelation to FIGS. 1A-3B. Accordingly, FIG. 4 is described with referenceto FIGS. 1A-3B.

At 402, the process 400 gathers training set data including historicalthin-film data and historical process conditions data. This can beaccomplished by using a data mining system or process. The data miningsystem or process can gather training set data by accessing one or moredatabases associated with the semiconductor process system 200 andcollecting and organizing various types of data contained in the one ormore databases. The data mining system or process, or another system orprocess, can process and format the collected data in order to generatea training set data. The training set data 306 can include historicalthin-film data 308 and historical process conditions data 310 asdescribed in relation to FIG. 3A.

At 404, the process 400 inputs historical process conditions data to theanalysis model. In one example, this can include inputting historicalprocess conditions data 310 into the analysis model 302 with thetraining module 304 as described in relation to FIG. 3A. The historicalprocess conditions data can be provided in consecutive discrete sets tothe analysis model 302. Each district set can correspond to a singlethin-film etching process or a portion of a single thin-film etchingprocess. The historical process conditions data can be provided asvectors to the analysis model 302. Each set can include one or morevectors formatted for reception processing by the analysis model 302.The historical process conditions data can be provided to the analysismodel 302 in other formats without departing from the scope of thepresent disclosure.

At 406, the process 400 generates predicted thin-film data based onhistorical process conditions data. In particular, the analysis model302 generates, for each set of historical thin-film conditions data 310,predicted thin-film data. The predicted thin-film data corresponds to aprediction of characteristics, such as the remaining thickness, of athin film that would result from that particular set of processconditions. The predicted thin-film data can include thickness,uniformity, composition, crystal structure, or other aspects of aremaining thin film.

At 408, the predicted thin-film data is compared to the historicalthin-film data 308. In particular, the predicted thin-film data for eachset of historical process conditions data is compared to the historicalthin-film data 308 associated with that set of historical processconditions data. The comparison can result in an error functionindicating how closely the predicted thin-film data matches thehistorical thin-film data 308. This comparison is performed for each setof predicted thin-film data. In one embodiment, this process can includegenerating an aggregated error function or indication indicating how thetotality of the predicted thin-film data compares to the historicalthin-film data 308. These comparisons can be performed by the trainingmodule 304 or by the analysis model 302. The comparisons can includeother types of functions or data than those described above withoutdeparting from the scope of the present disclosure.

At 410, the process 400 determines whether the predicted thin-film datamatches the historical thin-film data based on the comparisons generatedat step 408. For example, the process determines whether the predictedremaining thickness matches the actual remaining thickness after ahistorical etching process. In one example, if the aggregate errorfunction is less than an error tolerance, then the process 400determines that the thin-film data does not match the historicalthin-film data. In one example, if the aggregate error function isgreater than an error tolerance, then the process 400 determines thatthe thin-film data does match the historical thin-film data. In oneexample, the error tolerance can include a tolerance between 0.1 and 0.In other words, if the aggregate percentage error is less than 0.1, or10%, then the process 400 considers that the predicted thin-film datamatches the historical thin-film data. If the aggregate percentage erroris greater than 0.1 or 10%, then the process 400 considers that thepredicted thin-film data does not match the historical thin-film data.Other tolerance ranges can be utilized without departing from the scopeof the present disclosure. Error scores can be calculated in a varietyof ways without departing from the scope of the present disclosure. Thetraining module 304 or the analysis model 302 can make thedeterminations associated with process step 410.

In one embodiment, if the predicted thin-film data does not match thehistorical thin-film data 308 at step 410, then the process proceeds tostep 412. At step 412, the process 400 adjusts the internal functionsassociated with the analysis model 302. In one example, the trainingmodule 304 adjusts the internal functions associated with the analysismodel 302. From step 412, the process returns to step 404. At step 404,the historical process conditions data is again provided to the analysismodel 302. Because the internal functions of the analysis model 302 havebeen adjusted, the analysis model 302 will generate different predictedthin-film data that in the previous cycle. The process proceeds to steps406, 408 and 410 and the aggregate error is calculated. If the predictedthin-film data does not match the historical thin-film data, then theprocess returns to step 412 and the internal functions of the analysismodel 302 are adjusted again. This process proceeds in iterations untilthe analysis model 302 generates predicted thin-film data that matchesthe historical thin-film data 308.

In one embodiment, if the predicted thin-film data matches thehistorical thin-film data then process step 410, in the process 400,proceeds to 414. At step 414 training is complete. The analysis model302 is now ready to be utilized to identify process conditions and canbe utilized in thin-film etching processes performed by thesemiconductor process system 200. The process 400 can include othersteps or arrangements of steps than shown and described herein withoutdeparting from the scope of the present disclosure.

FIG. 5 is a flow diagram of a process 500 for dynamically selectingprocess conditions for thin-film etching process and for performing athin-film etching process, according to one embodiment. The varioussteps of the process 500 can utilize components, processes, andtechniques described in relation to FIGS. 1A-4 . Accordingly, FIG. 5 isdescribed with reference to FIGS. 1A-4 .

At 502, the process 500 provides target thin-film conditions data to theanalysis model 302. The target thin-film conditions data identifiesselected characteristics of a thin film to be formed by thin-filmetching process. The target thin-film conditions data can include atarget remaining thickness, a target composition, target crystalstructure, or other characteristics of the thin film. The targetthin-film conditions data can include a range of thicknesses. The targetcondition or characteristics that can be selected are based on thin filmcharacteristic(s) utilized in the training process. In the example ofFIG. 5 , the training process focused on thin film thickness.

At 504, the process 500 provides static process conditions to theanalysis model 302. The static process conditions include processconditions that will not be adjusted for a next thin-film etchingprocess. The static process conditions can include the target devicepattern density indicating the density of patterns on the wafer on whichthe thin-film etching process will be performed. The static processconditions can include an effective plan area crystal orientation, aneffective plan area roughness index, an effective sidewall area of thefeatures on the surface of the semiconductor wafer, an exposed effectivesidewall tilt angle, an exposed surface film function group, an exposedsidewall film function group, a rotation or tilt of the semiconductorwafer, process gas parameters (materials, phase of materials, andtemperature of materials), a remaining amount of material fluid in thefluid sources 208 and 210, a remaining amount of fluid in the purgesources 212 and 214, a humidity within a process chamber, an age of anampoule utilized in the etching process, light absorption or reflectionwithin the process chamber, the length of pipes or conduits that willprovide fluids to the process chamber, or other conditions. The staticprocess conditions can include conditions other than those describedabove without departing from the scope of the present disclosure.Furthermore, in some cases, some of the static process conditions listedabove may be dynamic process conditions subject to adjustment as will bedescribed in more detail below. In the example of FIG. 5 , dynamicprocess conditions include temperature, pressure, humidity, and flowrate. Static process conditions include phase, ampoule age, etchingarea, etching density, and sidewall angle.

At 506, the process 500 selects dynamic process conditions for theanalysis model, according to one embodiment. The dynamic processconditions can include any process conditions not designated as staticprocess conditions. For example, the training set data may include alarge number of various types of process conditions data in thehistorical process conditions data 310. Some of these types of processconditions will be defined the static process conditions and some ofthese types of process conditions will be defined as dynamic processconditions. Accordingly, when the static process conditions are suppliedat step 504, the remaining types of process conditions can be defined asdynamic process conditions. The analysis model 302 can initially selectinitial values for the dynamic process conditions. After the initialvalues have been selected for the dynamic process conditions, theanalysis model has a full set of process conditions to analyze. In oneembodiment, the initial values for the dynamic process conditions may beselected based on previously determined starter values, or in accordancewith other schemes.

The dynamic process conditions can include the flow rate of fluids ormaterials from the fluid sources 208 and 210 during the etching process.The dynamic process conditions can include the flow rate of fluids ormaterials from the purge sources 212 and 214. The dynamic processconditions can include a pressure within the process chamber, atemperature within the process chamber, a humidity within the processchamber, durations of various steps of the etching process, or voltagesor electric field generated within the process chamber. The dynamicprocess conditions can include other types of conditions withoutdeparting from the scope of the present disclosure.

At 508, the analysis model 302 generates predicted thin-film data basedon the static and dynamic process conditions. The predicted thin-filmdata includes the same types of thin-film characteristics established inthe target thin-film conditions data. In particular, the predictedthin-film data includes the types of predicted thin-film data from thetraining process described in relation to FIGS. 2A-4 . For example, thepredicted thin-film data can include thin-film thickness, filmcomposition, or other parameters of thin films.

At 510, the process compares the predicted thin-film data to the targetthin-film data. In particular, the analysis model 302 compares thepredicted thin-film data to the target thin-film data. The comparisonindicates how closely the predicted thin-film data matches the targetthin-film data. The comparison can indicate whether or not predictedthin-film data falls within tolerances or ranges established by thetarget thin-film data. For example, if the target thin-film thickness isbetween 1 nm and 9 nm, then the comparison will indicate whether thepredicted thin-film data falls within this range.

At 512, if the predicted thin-film data does not match the targetthin-film data, then the process proceeds to 514. At 514, the analysismodel 302 adjusts the dynamic process conditions data. From 514 theprocess returns to 508. At 508, the analysis model 302 again generatespredicted thin-film data based on the static process conditions and theadjusted dynamic process conditions. The analysis model then comparesthe predicted thin-film data to the target thin-film data at 510. At512, if the predicted thin-film data does not match the target thin-filmdata, then the process proceeds to 514 and the analysis model 302 againadjusts the dynamic process conditions. This process proceeds untilpredicted thin-film data is generated that matches the target thin-filmdata. If the predicted thin-film data matches the target thin-film data512, then the process proceeds to 516.

At 516, the process 500 adjusts the thin-film process conditions of thesemiconductor process system 200 based on the dynamic process conditionsthat resulted in predicted thin-film data within the target thin-filmdata. For example, the control system 224 can adjust fluid flow rates,etching step durations, pressure, temperature, humidity, or otherfactors in accordance with the dynamic process conditions data.

At 518, the semiconductor process system 200 performs a thin-filmetching process in accordance with the adjusted dynamic processconditions identified by the analysis model. In one embodiment, thethin-film etching process is an ALE process. However, other thin-filmetching processes can be utilized without departing from the scope ofthe present disclosure. In one embodiment, the semiconductor processsystem 200 adjusts the process parameters based on the analysis modelbetween individual etching stages in a thin-film etching process. Forexample, in an ALE process, the thin-film is etched one layer at a time.The analysis model 302 can identify parameters to be utilized foretching of the next layer. Accordingly, the semiconductor process systemcan adjust etching conditions between the various etching stages.

In one embodiment, an integrated circuit includes an interleveldielectric layer and a transistor. The transistor includes a trenchformed in the interlevel dielectric layer, a gate dielectric positionedon sidewalls of the trench and on a bottom of the trench, and a gateelectrode. The gate electrode includes a gate metal positioned on thegate dielectric at the bottom of the trench and a conductive gate fillmaterial positioned over the gate metal in the trench. The conductivegate fill material extends to a higher vertical level within the trenchthan the gate metal.

In one embodiment, a method includes forming a first trench in aninterlevel dielectric layer over a plurality of first semiconductornanosheets corresponding to channel regions of a first transistor. Themethod includes depositing a gate dielectric on a bottom of the firsttrench, depositing a first gate metal of the transistor in the firsttrench on the gate dielectric, and filling the first trench with aconductive gate fill material over the first gate metal. The conductivegate fill material extends to a higher vertical level within the firsttrench than does the first gate metal.

In one embodiment, a method includes training an analysis model with amachine learning process to select parameters for an atomic layeretching process. The method includes depositing a gate metal of atransistor in a trench in an interlevel dielectric layer of anintegrated circuit, selecting, with the analysis model, etchingparameters for etching the gate metal, and etching the gate metal withthe atomic layer etching process based on the selected etchingparameters.

The various embodiments described above can be combined to providefurther embodiments. All U.S. patent application publications and U.S.patent applications referred to in this specification and/or listed inthe Application Data Sheet are incorporated herein by reference, intheir entirety. Aspects of the embodiments can be modified, ifnecessary, to employ concepts of the various patents, applications andpublications to provide yet further embodiments.

These and other changes can be made to the embodiments in light of theabove-detailed description. In general, in the following claims, theterms used should not be construed to limit the claims to the specificembodiments disclosed in the specification and the claims, but should beconstrued to include all possible embodiments along with the full scopeof equivalents to which such claims are entitled. Accordingly, theclaims are not limited by the disclosure.

8673666_1

The invention claimed is:
 1. An integrated circuit, comprising: aninterlevel dielectric layer; a first transistor including: a firsttrench formed in the interlevel dielectric layer; a gate dielectricpositioned on sidewalls of the first trench and on a bottom of the firsttrench; a gate electrode, including: a first gate metal positioned onthe gate dielectric at the bottom of the first trench, wherein the firstgate metal has a substantially planar top surface extending betweenportions of the gate dielectric on the sidewalls of the trench; and aconductive gate fill material positioned over the first gate metal inthe first trench, wherein the conductive gate fill material extends to ahigher vertical level within the first trench than the first gate metal,wherein the gate dielectric extends to a same vertical level within thefirst trench as the conductive gate fill metal.
 2. The integratedcircuit of claim 1, wherein the first transistor includes a pluralitysemiconductor nanosheets below the trench, wherein the semiconductornanosheets are channel regions of the first transistor.
 3. Theintegrated circuit of claim 2, wherein the gate dielectric and the gateelectrode surround the semiconductor nanosheets.
 4. The integratedcircuit of claim 1, further comprising: a second transistor including: asecond trench formed in the interlevel dielectric layer; the gatedielectric positioned on sidewalls of the second trench and on a bottomof the second trench; a gate electrode, including the conductive gatefill material positioned in the second trench above the gate dielectric,the conductive gate fill material of the second transistor beingpositioned closer to the bottom of the second trench than the conductivegate fill material of the first transistor is positioned to the bottomof the first trench.
 5. The integrated circuit of claim 4, wherein thefirst transistor has a higher threshold voltage than the secondtransistor.
 6. The integrated circuit of claim 4, further comprising: athird transistor including: a third trench formed in the interleveldielectric layer; the gate dielectric positioned on sidewalls of thethird trench and on a bottom of the third trench; a gate electrode,including: the first gate metal positioned on the gate dielectric at abottom of the third trench; a second gate metal positioned on the firstgate metal; the conductive gate fill material positioned over the firstgate metal and the second gate metal in the third trench and extendingto a higher vertical level in the third trench than the first and secondgate metals.
 7. The integrated circuit of claim 6, wherein theconductive gate fill material of the third transistor is positionedcloser to the bottom of the second trench than the conductive gate fillmaterial of the first transistor is positioned to the bottom of thesecond trench.
 8. The integrated circuit of claim 7, wherein the thirdtransistor has a higher threshold voltage than the first transistor. 9.The integrated circuit of claim 4, wherein the first and second gatemetals are absent from the second trench.
 10. The integrated circuit ofclaim 1, wherein the first gate metal is titanium nitride.
 11. Theintegrated of claim 1, wherein the conductive gate fill materialincludes cobalt.
 12. A method, comprising: forming a first trench in aninterlevel dielectric layer over a plurality of first semiconductornanosheets corresponding to channel regions of a first transistor;depositing a gate dielectric on a bottom of the first trench and onsidewalls of the first trench; depositing a first gate metal of thetransistor in the first trench on the first gate dielectric, wherein thefirst gate metal has a substantially planar top surface extendingbetween portions of the gate dielectric on the sidewalls of the trench;and filling the first trench with a conductive gate fill material overthe first gate metal, wherein the conductive gate fill material extendsto a higher vertical level within the first trench than does the firstgate metal, wherein the gate dielectric extends to the vertical level ofthe conductive gate fill material within the first trench.
 13. Themethod of claim 12, further comprising: selecting parameters for anatomic layer etching process of the first gate metal; and prior tofilling the first trench with the conductive gate fill material,patterning the first gate metal within the first trench with the atomiclayer etching process.
 14. The method of claim 13, further comprisingselecting the parameters for the atomic layer etching process with ananalysis model trained with a machine learning process.
 15. The methodof claim 12, further comprising: forming a second trench in theinterlevel dielectric layer over a plurality of second semiconductornanosheets corresponding to channel regions of a second transistor;depositing the gate dielectric on a bottom of the second trench; andfilling the second trench with the conductive gate fill material,wherein the conductive gate fill material of the second transistor ispositioned closer to the bottom of the second trench than the conductivegate fill material of the first transistor is positioned to the bottomof the first trench.
 16. The method of claim 15, wherein the firsttransistor has a higher threshold voltage than the second transistor.17. A method, comprising: training an analysis model with a machinelearning process to select parameters for an atomic layer etchingprocess; depositing a gate dielectric of a transistor in a trench in aninterlevel dielectric layer of an integrated circuit; depositing a gatemetal of the transistor on the gate dielectric in the trench; selecting,with the analysis model, etching parameters for etching the gate metal;etching the gate metal with the atomic layer etching process based onthe selected etching parameters, wherein after the etching the gatemetal has a substantially planar top surface extending between portionsof the gate dielectric on the sidewalls of the trench and the gatedielectric extends to a higher vertical level within the trench thandoes the gate metal; and depositing a conductive gate fill materialpositioned over the gate metal in the first trench, wherein theconductive wherein the gate dielectric extends to a same vertical levelwithin the first trench as the conductive gate fill metal.
 18. Themethod of claim 17, wherein the selected parameters include a number ofatomic layer etching cycles.
 19. The method of claim 18, wherein theselected parameters include a flow rate of an etching fluid.
 20. Themethod of claim 17, wherein the analysis model selects the parametersbased, in part, on a selected remaining thickness of the gate metal.